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Identification of cellular senescence-associated genes for predicting the diagnosis, prognosis and immunotherapy response in lung adenocarcinoma via a 113-combination machine learning framework

Crossref DOI link: https://doi.org/10.1007/s12672-025-02262-3

Published Online: 2025-04-01

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Ge, Ting

He, Guixin

Cui, Qian

Wang, Shuangcui

Wang, Zekun

Xie, Yingying

Tian, Yuanyuan

Zhou, Juyue

Yu, Jianchun

Hu, Jinmin

Li, Wentao
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Text and Data Mining valid from 2025-04-01

Version of Record valid from 2025-04-01
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Article History

Received: 10 December 2024

Accepted: 28 March 2025

First Online: 1 April 2025

Declarations

:

: The authors declare no competing interests.

: The study is based on the data available in the public domain to use; therefore, no ethics statement is required for this work. For GEO datasets and TCGA database, ethical review and approval can be accessed in the original studies.

: Not applicable.

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